Maximum Margin Clustering Using Extreme Learning Machine

Maximum margin clustering (MMC) is a newly proposed clustering method, which extends large margin computation of support vector machine (SVM) to unsupervised learning. But in nonlinear cases, time complexity is still high. Since extreme learning machine (ELM) has achieved similar generalization performance at much faster learning speed than traditional SVM and LS-SVM, we propose an extreme maximum margin clustering (EMMC) algorithm based on ELM. It can perform well in nonlinear cases. Moreover, the kernel parameters of EMMC need not be tuned by means of random feature mappings. Experimental results on several real-world data sets show that EMMC performs better than traditional MMC methods, especially in handling large scale data sets. Streszczenie. Opisano nową metodę klastrowania „maximum margin clusterung MMC” która rozszerza wielkość marginesu obliczeń numerycznych w systemie SVM z uczeniem bez nadzoru. Nowa metoda EMMC (extreme maximum margin clustering) zapewnia szybsze uczenie, szczególnie w warunkach nieliniowości. (Nowa metoda klastrowania – extreme margin clustering EMC w systemach extreme learning machine ELM)